The article [1]} describes the CAE architecture principles. The authors describe general applications of the autoencoders, as image denoising, or feature space reduction. They also compare CAEs with fully connected autoencoders and describe the advantages of CAEs. In this article, authors also provide a mathematical background of the CAE and show the importance of the max-pooling layer for the learning consistency. A possibility of initializing the classification CNN with the same topology CAE is explained including its benefits.
